• Go to this page (or use the QR code): https://bit.ly/datahandling-2022
• Use one row to respond to the questions in the column headers (see the first two rows for examples).

“This coupling of scientific discovery and practice involves the collection, management, processing, analysis, visualization, and interpretation of vast amounts of heterogeneous data associated with a diverse array of scientific, translational, and inter-disciplinary applications.”

University of Michigan ‘Data Science Initiative’, 2015

“Seemingly, statistics is being marginalized here; the implicit message is that statistics is a part of what goes on in data science but not a very big part. At the same time, many of the concrete descriptions of what the DSI will actually do will seem to statisticians to be bread-and-butter statistics. Statistics is apparently the word that dare not speak its name in connection with such an initiative!”

David Donoho (2015). 50 years of Data Science

“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: …”

“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

• 2017-today: Assistant Professor of Economics, University of St.Gallen
• 2021-today: Guest Lecturer (Applied Data Science), University of Lucerne

Previously:

Research:

• Can the personalization of Google search results lead to political polarization?
• Does YouTube’s recommender algorithm lead to radicalization?
• Do politicians vote in the interest of money donors when voters are distracted?

Teaching:

• Well, this course…
• Big Data Analytics (Master)
• Introduction to Web Mining (Master)
• Economics in Practice (Master)

• Lectures (Thursday morning)
  • Background/Concepts
  • Illustration concepts
  • Illustration of ‘hands-on’ approaches

• 27.10.2022: Research insights
  • Ulrich Matter: Web Data
  • Aurélien Sallin: Text as Data
  • Michael Tüting: Images as Data

• Exercise sheets (handed out every other week)
  • Some conceptual questions
  • Hands-on exercises/tutorials in R
  • Detailed solution videos
  • First Exercises (set up R/RStudio) is available on StudyNet/Canvas today

• Learning mode in this course: Prepare with reading, visit the lecture, recap key concepts in lecture notes (self-study), work on exercises, watch solution video, come to exercise session, repeat…

• Strongly encouraged: (virtual) learning groups!

  • Biweekly exercises provide opportunity.
  • Tackle the tricky exercises together!

• In-class exercise sessions (bi-weekly evening sessions)
  • Discussion of exercises and additional input
  • Recap of concepts
  • Q&A, support
  • time for more coding!

• All information and materials (notes, slides, course sheet, syllabus, etc.) are available on StudyNet/Canvas.
• Core materials will also be made available on Nuvolos.

Data Handling Pocket Reference

Murrell, Paul (2009). Introduction to Data Technologies, London: Chapman & Hall/CRC.

Wickham, Hadley and Garred Grolemund (2017). R for Data Science, 1st Edition. Sebastopol, CA: O’Reilly.

• Stackoverflow
• Get inspired in the R blogsphere

• Central, written examination: digital, BYOD!, we will have an instructional session by the head of the digital examinations team (data TBD).
• Multiple choice questions.
• A few open questions.
• Theoretical concepts and practical applications in R (questions based on code examples).

• We will release samples of multiple choice questions via Quizzes on Canvas/Studynet (exact same format and style of exam questions).
• Exchange students who need to take the exam before the central exam block:
  • Date, time place, : 22.12.2022, 16:15-18:00, room 01-013.
  • Questions: michael.tueting@unisg.ch